Behavior of citizens or clients in the domains of mobility and retail is typically studied using surveys that poll in detail the daily activities of observed people using paper or online questionnaires. E.g. as transportation planning models move towards the use of micro-simulations of daily activities and travel patterns, there is pressure to increase the quantity and quality of travel survey data to feed the micro-models. Conventional survey methods (e.g. Dutch national travel survey) require participants to log all their activities and trips through paper diaries, and self-report them as accurately as possible. These self-reported surveys face problems including the fact that typically small sample sizes are involved, the fact that overtime, there are increasing non-response rates, the fact that there are a number of non-representative samples, the fact that there are a number of missing activities and trips, the fact that there may be imprecise travel time, etc.
Due to the burden on the respondents as well as the decrease in the quality of the recorded data, paper or web based diaries are used for capturing only a couple of days of travel behavior. GPS-based data collection methods have been introduced that are potentially more accurate and less of a burden on respondents compared to paper diary methods, as exact location coordinates of trip destinations, trip routes and travel times can be recorded. GPS-based data collection is performed using dedicated data loggers or smartphones. The main bottleneck with GPS is battery drain. On current battery technology, mobile GPS logging typically lasts 4 hours, which is insufficient to follow a person throughout a day. GPS also only works outdoors with a clear satellite view and is unable to follow activity indoors (e.g. shopping, car parks) and in built-up urban areas. Gong et al 2012 describes a GPS/GIS (Geographic Information System) for travel mode detection. They introduce a high level GIS interpretation to setup a rule based system which interprets speed and proximity patterns around locations where chances are high that users change from transport mode (e.g. stations). A disadvantage is that this system, by design, only works when a GPS system is available (the GIS rules also need spatial information). As a GPS system is power consuming this poses issues on the possible duration of the logging. A second disadvantage is that the rules only work in strict well defined cases.
Another solution exists in the usage of the accelerometer sensor, which is present in current smartphones or through the use of ad-hoc sensors. In this approach, 3D-accelerometer data is used to identify the transportation mode of the user (pedestrian, bicycle, car) through pattern recognition. Being more battery-conservative, logging can be performed over long periods of time while the user carries the device or smartphone in his pocket. Accuracy depends on the number transport mode classes and the sampling frequency used and varies between 70 and 90%. In this way, a daily travel pattern of a user (tour) can be segmented into different trip legs along the time axis, each belonging to a different transport mode. Main drawback of accelerometer tracking is that time logging is insufficient for detailed reporting in behavior studies. Without location data, the purpose of trip cannot be identified (e.g. work, school, shop) and need to be queried additionally from the user. Another difficulty is the reporting of change of transport mode. While accelerometer logging can report mode changes at given points in time, they cannot identify waiting times at mode changes. E.g. a commuter walking to a bus station to catch his bus will be seen walking and changing to a bus. The time spent waiting at the bus station can typically not be identified as this is classified as walking (within the station). Wait times are however crucial in different applications like the management of public transport.
Manzoni et al 2010 report such a transportation mode classification system based on accelerometer patterns. The accuracy of this system is reported to be 82% however this system only identifies the transport mode.
As mentioned previously GPS poses heavy power constraints on tracking systems. FP7 sunset reports on a battery conservative sampling strategy to collect GPS measurements from a mobile device. It uses low-sampled GPS polling to detect movement, which can trigger higher frequency polling. However also here the system only identifies the transport mode.
Current technology does not allow to unambiguously observe a person's transportation mode/trip purpose/etc. . . . One has to rely on sensor measurements like accelerometer, gps, . . . to get an indication of this state. Clearly, those sensor outputs are the result of a stochastic process and it is rarely possible to determine the true state with absolute confidence. Furthermore, it is clear that knowing only the transport mode is often insufficient for mobility studies.
Therefore there is still room for improvement for systems and methods trying to solve the problem of constructing activity behavior models of people in domains like mobility and retail.